Experience Level: 6 months-1 year
Function: Engineering — AI / Full Stack Development
No. of vacancies: 4

About the Role
cloud-native infrastructure. We’re looking for early-career engineers who have a solid grasp of AI/NLP
fundamentals and full-stack development basics, and are eager to grow into building production-grade AI

What You’ll Do
Assist in building and integrating RAG pipelines and LLM-powered features
Develop front-end interfaces using React under guidance from senior engineers
Build and maintain backend APIs using Node.js/Python
Work with vector databases and LangChain for retrieval-based features
Support deployment and testing of AI workloads on AWS
Learn and follow team practices for code quality, version control, and testing
Collaborate with product, design, and QA, and communicate progress/blockers clearly

Must-Have Skills

Programming & AI/NLP Fundamentals
Proficiency in Python
Understanding of NLP basics: tokenization, text preprocessing, POS tagging, NER
Text vectorization concepts: BoW, TF-IDF, Word2Vec/embeddings — should be able to explain and implement
Conceptual understanding of transformer architecture (self-attention, positional encoding)
High-level understanding of how LLMs (GPT/LLaMA-class models) work




Exposure to RAG concepts and vector databases (FAISS or ChromaDB) — academic or personal projects acceptable
Basic hands-on experience with LangChain (document loaders, text splitters, straightforward chains)

Full Stack Development
React fundamentals — components, props, state, hooks, JavaScript/TypeScript, HTML, CSS
Node.js basics — building simple REST APIs
Git — clone, commit, branch, pull requests
Basic SQL/NoSQL — CRUD operations Cloud (AWS) — Conceptual
Conceptual awareness of Lambda, DynamoDB, and IAM — what they are and when they’re used

Soft Skills
Solid communication — written and verbal
Willingness to learn independently and ask the right questions
Problem-solving attitude — takes initiative rather than waiting for exact instructions

Nice-to-Have
A personal or academic RAG/chatbot project (end-to-end, however small)
Exposure to MCP, LangGraph, or agentic frameworks
Basic fine-tuning exposure (LoRA/QLoRA) via coursework or tutorials
Self-learned, free-tier hands-on experience with AWS Bedrock or SageMaker
Any exposure to Atlassian Forge, Bitbucket, or Jest/unit testing

Department :Delivery

Designation :Engineer

📌 Associate Ai Full Stack Engineer Pune
🏢 enreap
📍 Pune